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Found 11 papers, 2 papers with code
Joint Probability Selection and Power Allocation for Federated Learning
The federated learning performance depends on the selection of the clients participating in the learning at each round.
Demystifying the Myths and Legends of Nonconvex Convergence of SGD
Additionally, our analyses allow us to measure the density of the $\epsilon$-stationary points in the final iterates of SGD, and we recover the classical $O(\frac{1}{\sqrt{T}})$ asymptotic rate under various existing assumptions on the objective function and the bounds on the stochastic gradient.
Ensemble DNN for Age-of-Information Minimization in UAV-assisted Networks
To tackle this problem, we first derive a closed-form expression of the expected AoI that involves the probabilities of selection of devices.
A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems
In this paper, we analyze the convergence of RK for noisy linear systems when the coefficient matrix, $A$, is corrupted with both additive and multiplicative noise, along with the noisy vector, $b$.
Muti-Agent Proximal Policy Optimization For Data Freshness in UAV-assisted Networks
Our objective is to optimally design the UAVs' trajectories and the subsets of visited IoT devices such as the global Age-of-Updates (AoU) is minimized.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Linear Scalarization for Byzantine-robust learning on non-IID data
In this work we study the problem of Byzantine-robust learning when data among clients is heterogeneous.
Minibatch Stochastic Three Points Method for Unconstrained Smooth Minimization
In this paper, we propose a new zero order optimization method called minibatch stochastic three points (MiSTP) method to solve an unconstrained minimization problem in a setting where only an approximation of the objective function evaluation is possible.
Personalized Federated Learning with Communication Compression
Recently, Hanzely and Richt\'{a}rik (2020) proposed a new formulation for training personalized FL models aimed at balancing the trade-off between the traditional global model and the local models that could be trained by individual devices using their private data only.
Client Selection in Federated Learning based on Gradients Importance
We investigate and design a device selection strategy based on the importance of the gradient norms.
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning
Compressed communication, in the form of sparsification or quantization of stochastic gradients, is employed to reduce communication costs in distributed data-parallel training of deep neural networks.
Direct Nonlinear Acceleration
In contrast to RNA which computes extrapolation coefficients by (approximately) setting the gradient of the objective function to zero at the extrapolated point, we propose a more direct approach, which we call direct nonlinear acceleration (DNA).
